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Record W4323922178 · doi:10.1016/j.paid.2023.112152

Dispositioned to resist? The Big Five and resistance to dissonant political views

2023· article· en· W4323922178 on OpenAlexfundno aff
Chiara Valli, Alessandro Nai

Bibliographic record

VenuePersonality and Individual Differences · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsnot available
FundersInstitute of Population and Public HealthUniversity of Bern
KeywordsConsonance and dissonanceOpenness to experiencePsychologyPoliticsSocial psychologyResistance (ecology)Big Five personality traitsNeuroticismAffect (linguistics)RuminationPersonalityExtraversion and introversionCognitionCognitive dissonancePolitical scienceCommunicationLaw

Abstract

fetched live from OpenAlex

This article investigates how dispositional traits influence the way individuals resist dissonant political information. More specifically, the relationship between the Big Five personality traits and four resistance strategies (avoidance, contesting, empowering, and negative affect) is explored. To do so, we present new evidence from an online survey where respondents from a Swiss sample (N = 936) were exposed to tailored counterarguments on a political initiative and asked to report their cognitive, behavioral, and affective responses to the dissonant messages. Against our expectations, openness is unrelated to any type of resistance. Conscientious individuals are hesitant to actively resist counter-attitudinal political information, while extraverts defend their attitude by bolstering their preexisting views. Similar tendencies are visible for agreeable respondents, although these individuals primarily rely on avoiding dissonant political content. Individuals high on neuroticism exhibit a strong emotional response by reacting with negative affect to oppositional political information.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.841
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.127
GPT teacher head0.373
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2023
Admission routes1
Has abstractyes

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